Adjustment method for microarray data generated using two-cycle RNA labeling protocol
- Equal contributors
1 Center for Quantitative Biology, Peking University, Beijing, 100871, China
2 LMAM,School of Mathematical Sciences, Peking University, Beijing, 100871, China
3 School of Life Science, Peking University, Beijing, 100871, China
4 Center for Statistical Sciences, Peking University, Beijing, 100871, China
BMC Genomics 2013, 14:31 doi:10.1186/1471-2164-14-31Published: 16 January 2013
Microarray technology is widely utilized for monitoring the expression changes of thousands of genes simultaneously. However, the requirement of relatively large amount of RNA for labeling and hybridization makes it difficult to perform microarray experiments with limited biological materials, thus leads to the development of many methods for preparing and amplifying mRNA. It is addressed that amplification methods usually bring bias, which may strongly hamper the following interpretation of the results. A big challenge is how to correct for the bias before further analysis.
In this article, we observed the bias in rice gene expression microarray data generated with the Affymetrix one-cycle, two-cycle RNA labeling protocols, followed by validation with Real Time PCR. Based on these data, we proposed a statistical framework to model the processes of mRNA two-cycle linear amplification, and established a linear model for probe level correction. Maximum Likelihood Estimation (MLE) was applied to perform robust estimation of the Retaining Rate for each probe. After bias correction, some known pre-processing methods, such as PDNN, could be combined to finish preprocessing. Then, we evaluated our model and the results suggest that our model can effectively increase the quality of the microarray raw data: (i) Decrease the Coefficient of Variation for PM intensities of probe sets; (ii) Distinguish the microarray samples of five stages for rice stamen development more clearly; (iii) Improve the correlation coefficients among stamen microarray samples. We also discussed the necessity of model adjustment by comparing with another simple adjustment method.
We conclude that the adjustment model is necessary and could effectively increase the quality of estimation for gene expression from the microarray raw data.